Optimization and Validation of Efficient Models for Predicting Polythiophene Self-Assembly

Miller, Evan; Jones, Matthew L.; Henry, Michael M.; Chery, Paul; Miller, Judy Z.

doi:10.3390/polym10121305

Cited by 14 publications

(17 citation statements)

References 74 publications

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“…ψ′ is a quantification of fraction of thiophene rings composed into “large” clusters and the deviations in the aliphatic bond lengths. A description of the origins and implementation of ψ′ is included in Supporting Information Section 5 and References and . Each morphology is composed of 15,000 P3HT repeat units, giving about 230,000 chromophore pairs (as defined by the Voronoi tessellation around thiophene centers).…”

Section: Methodsmentioning

confidence: 99%

“…In our own prior work, we predict charge transport through P3HT by first predicting P3HT morphologies at ∼350 processing state points (Supporting Information Section 1), then calculating charge mobility through ∼100 of these structures using KMC simulations. Doing so requires hopping rates between P3HT chromophores, which we calculate with Marcus semi‐classical hopping theory using quantum chemical ZINDO/S calculations to obtain the electronic transfer integrals between chromophores (couplings, J i,j ), which describe the amount of frontier molecular orbital overlap between pairs chromophores.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning predictions of electronic couplings for charge transport calculations of P3HT

et al. 2019

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The purpose of this work is to lower the computational cost of predicting charge mobilities in organic semiconductors, which will benefit the screening of candidates for inexpensive solar power generation. We characterize efforts to minimize the number of expensive quantum chemical calculations we perform by training machines to predict electronic couplings between monomers of poly‐(3‐hexylthiophene). We test five machine learning techniques and identify random forests as the most accurate, information‐dense, and easy‐to‐implement approach for this problem, achieving mean‐absolute‐error of 0.02 [× 1.6 × 10−19 J], R2 = 0.986, predicting electronic couplings 390 times faster than quantum chemical calculations, and informing zero‐field hole mobilities within 5% of prior work. We discuss strategies for identifying small effective training sets. In sum, we demonstrate an example problem where machine learning techniques provide an effective reduction in computational costs while helping to understand underlying structure–property relationships in a materials system with broad applicability.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning predictions of electronic couplings for charge transport calculations of P3HT

et al. 2019

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“…Recent work by Jankowski et al . with more sophisticated theorectical treatments, in which they combined the large simulation volumes from optimized molecular dynamics (MD) simulations of P3HT with quantum chemical calculations (QCC)‐informed charge transport, further bolsters the critical role of tie‐chain connectivity in charge transport …”

Section: Bridging Electrical Properties and Morphologymentioning

confidence: 99%

The Polymer Physics of Multiscale Charge Transport in Conjugated Systems

Loo

2019

J Polym Sci B Polym Phys

102

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Conjugated polymers are promising candidates for next‐generation low‐cost flexible electronics. Field‐effect transistors comprising conjugated polymers have witnessed significant improvements in device performance, notably the field‐effect mobility, in the last three decades. However, to truly make these materials commercially competitive, a better understanding of charge‐transport mechanisms in these structurally heterogeneous systems is needed for providing systematic guides for further improvements. This review assesses the key microstructural features of conjugated polymers across multiple length scales that can influence charge transport, with special attention given to the underlying polymer physics. The mechanistic understanding from collective experimental and theoretical studies point to the importance of interconnected ordered domains given the macromolecular nature of the polymeric semiconductors. Based on the criterion, optimization to improve charge transport can be broadly characterized by efforts to (a) promote intrachain transport, (b) establish intercrystallite connectivity, and (c) enhance interchain coupling. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019, 57, 1559–1571

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“…To engineer better OPVs and ameliorate global climate change it is necessary to answer (1) “Which nanostructures are required for high device efficiency?”, and (2) “What processing protocols are required to obtain these structures?” In this article, we address the first question by identifying structure-performance relationships for the benchmark donor material poly-(3-hexylthiophene) (P3HT). The second issue is investigated for P3HT in a companion work [12].…”

Section: Introductionmentioning

confidence: 99%

Tying Together Multiscale Calculations for Charge Transport in P3HT: Structural Descriptors, Morphology, and Tie-Chains

2018

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Evaluating new, promising organic molecules to make next-generation organic optoelectronic devices necessitates the evaluation of charge carrier transport performance through the semi-conducting medium. In this work, we utilize quantum chemical calculations (QCC) and kinetic Monte Carlo (KMC) simulations to predict the zero-field hole mobilities of ∼100 morphologies of the benchmark polymer poly(3-hexylthiophene), with varying simulation volume, structural order, and chain-length polydispersity. Morphologies with monodisperse chains were generated previously using an optimized molecular dynamics force-field and represent a spectrum of nanostructured order. We discover that a combined consideration of backbone clustering and system-wide disorder arising from side-chain conformations are correlated with hole mobility. Furthermore, we show that strongly interconnected thiophene backbones are required for efficient charge transport. This definitively shows the role “tie-chains” play in enabling mobile charges in P3HT. By marrying QCC and KMC over multiple length- and time-scales, we demonstrate that it is now possible to routinely probe the relationship between molecular nanostructure and device performance.

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Optimization and Validation of Efficient Models for Predicting Polythiophene Self-Assembly

Cited by 14 publications

References 74 publications

Machine learning predictions of electronic couplings for charge transport calculations of P3HT

Machine learning predictions of electronic couplings for charge transport calculations of P3HT

The Polymer Physics of Multiscale Charge Transport in Conjugated Systems

Tying Together Multiscale Calculations for Charge Transport in P3HT: Structural Descriptors, Morphology, and Tie-Chains

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